TY  - BOOK
AU  - Zhuang,Xiahai
AU  - Li,Lei
AU  - Wang,Sihan
AU  - Wu,Fuping
ED  - SpringerLink (Online service)
TI  - Left Atrial and Scar Quantification and Segmentation: First Challenge, LAScarQS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9783031317781
AV  - TA1501-1820 
U1  - 006 23
PY  - 2023///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Image processing
KW  - Digital techniques
KW  - Computer vision
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
N1  - Acceso multiusuario; LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification -- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network -- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation -- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners -- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net -- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation  -- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing -- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI  -- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images -- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium -- Deep U-Net architecture with curriculum learning for left atrial segmentation -- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion -- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation -- Automated segmentation of the left atrium and scar using deep convolutional neural networks -- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge
N2  - This book constitutes the First Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, which was held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022. The 15 papers presented in this volume were carefully reviewed and selected form numerous submissions. The aim of the challenge is not only benchmarking various LA scar segmentation algorithms, but also covering the topic of general cardiac image segmentation, quantification, joint optimization, and model generalization, and raising discussions for further technical development and clinical deployment
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-31778-1
ER  -